Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting

• URL: http://arxiv.org/abs/2410.20772v1
• Date: Mon, 28 Oct 2024 06:17:20 GMT
• Title: Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting
• Authors: Bong Gyun Kang, Dongjun Lee, HyunGi Kim, DoHyun Chung,
• Abstract summary: Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. They are constrained by their inherent inability to effectively address long-range dependencies in time series data. We introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples.
• Score: 8.458068118782519
• License:
• Abstract: Sequence modeling faces challenges in capturing long-range dependencies across diverse tasks. Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. However, they are constrained by their inherent inability to effectively address long-range dependencies in time series data, primarily due to using fixed-size inputs for prediction. Furthermore, they typically sacrifice essential temporal correlation among consecutive training samples by shuffling them into mini-batches. To overcome these limitations, we introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples and facilitates the handling of long-range information while maintaining the base model structure. Spectral Attention preserves long-period trends through a low-pass filter and facilitates gradient to flow between samples. Spectral Attention can be seamlessly integrated into most sequence models, allowing models with fixed-sized look-back windows to capture long-range dependencies over thousands of steps. Through extensive experiments on 11 real-world time series datasets using 7 recent forecasting models, we consistently demonstrate the efficacy of our Spectral Attention mechanism, achieving state-of-the-art results.

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